# Install necessary packages for sentiment analysis
install.packages(c("tidyverse", "tidytext", "textdata"))

library(tidyverse)
library(tidytext)
library(textdata)

comments <- tibble(
  id = 1:30,
  text = c(
    "AI is amazing and will make education so much better!",
    "I’m worried AI will take over jobs and leave people unemployed.",
    "AI helps doctors save lives, it’s a game-changer.",
    "I don’t trust AI, it feels creepy and invasive.",
    "AI is okay, but it needs regulation to be safe.",
    "AI in schools is cool, but it’s not perfect.",
    "Wow, AI is so great, it’ll solve all our problems… yeah, right!",  # Sarcasm
    "AI makes healthcare faster and more accurate, love it!",
    "Why does AI know so much about me? It’s unsettling.",
    "AI chatbots are fun to talk to, but sometimes useless.",
    "AI in movies is awesome, makes everything so realistic!",
    "I’m scared AI will control everything one day.",
    "AI helps me study better, it’s like a personal tutor.",
    "AI is overhyped, it’s not as smart as people think.",  # Mixed
    "Using AI for art is creative and inspiring!",
    "AI in cars? No way, I don’t trust self-driving tech.",
    "AI makes my phone so smart, it’s incredible!",
    "I feel like AI is watching me all the time, creepy.",
    "AI in gaming makes battles so epic, I’m hooked!",
    "AI might replace teachers, and that’s not cool.",
    "AI saves time at work, but I miss human interaction.",
    "AI’s fine, but it makes mistakes sometimes.",  # Neutral
    "AI in music creation is a total game-changer!",
    "I’m skeptical about AI making fair decisions.",
    "AI is great, but only if it’s used ethically.",  # Mixed
    "AI makes life easier, but it’s a bit scary too.",  # Mixed
    "AI in agriculture boosts crops, amazing stuff!",
    "I don’t get why everyone loves AI so much.",  # Negative
    "AI tutors are helpful, but they don’t replace real teachers.",
    "AI sounds cool, but I’m not sure it’s safe."  # Mixed
  )
)

colnames(comments)
nrow(comments)

comments$text[1]

words <- comments %>%
  unnest_tokens(word, text)

afinn <- get_sentiments("afinn")

sentiment_scores <- words %>%
  inner_join(afinn, by = "word")

comment_sentiment <- sentiment_scores %>%
  group_by(id) %>%
  summarize(total_score = sum(value)) %>%
  right_join(comments, by = "id") %>% 
  arrange(id)

comment_sentiment <- comment_sentiment %>% 
  mutate(sentiment = case_when(is.na(total_score) ~ NA_character_,
                               total_score > 0 ~ "Positive", 
                               total_score < 0 ~ "Negative",
                               TRUE ~ "Neutral"))

ggplot(comment_sentiment, aes(x = id, y = total_score, fill = sentiment)) +
  geom_bar(stat = "identity", na.rm = TRUE) +
  geom_text(
    data = filter(comment_sentiment, !is.na(total_score)),
    aes(
      label = total_score,
      vjust = case_when(
        total_score >= 0 ~ -0.3,
        total_score < 0  ~ 1.3
      )
    )
  ) +
  geom_text(
    data = filter(comment_sentiment, is.na(total_score)),
    aes(y = 0, label = "NA"),
    vjust = -0.3,
    color = "black",
    size = 2
  ) +
  labs(title = "Sentiment Scores of Comments about AI usign AFINN",
       x = "Comment ID", y = "Sentiment Score") +
  scale_fill_manual(
    name = "Sentiment",
    values = c("Negative" = "red", "Positive" = "blue"),
    labels = c("Negative", "Positive"),
    na.translate = FALSE # don't show NA in the legend
  ) +
  scale_x_continuous(breaks = seq(2, 30, by = 2)) +
  theme_minimal()

ggplot(comment_sentiment, aes(x = total_score, fill = sentiment)) +
  geom_density(alpha = 0.5) +
  labs(title = "Density Plot of Comment Sentiment Scores",
       x = "Sentiment Score", y = "Density") +
  theme_minimal()

ggplot(comment_sentiment, aes(x = sentiment, fill = sentiment)) +
  geom_bar() +
  geom_text(
    stat = "count",
    aes(label = after_stat(count)),
    vjust = -0.5
  ) +
  labs(title = "Histogram of Sentiment Labels",
       x = "Sentiment", y = "Count") +
  theme_minimal()

library(tidytext)

bing <- get_sentiments("bing")

nrc <- get_sentiments("nrc")

head(bing, 10)

head(nrc, 10)

words_bing <- words %>%
  inner_join(bing, by = "word")

words_nrc <- words %>%
  inner_join(nrc, by = "word")

comment_sentiment_bing <- words_bing %>%
  group_by(id, sentiment) %>%
  summarise(word_count = n(), .groups = "drop") %>%
  pivot_wider(names_from = sentiment, values_from = word_count, values_fill = 0) %>%
  right_join(comments, by = "id") %>% 
  mutate(total_score = positive - negative) %>%
  mutate(sentiment = case_when(
      is.na(total_score) ~ NA_character_,
      total_score > 0 ~ "Positive",
      total_score < 0 ~ "Negative",
      TRUE ~ "Neutral"
    )) %>% arrange(id)

words_nrc_pn <- words_nrc %>% filter(sentiment %in% c("positive", "negative"))

comment_sentiment_nrc <- words_nrc_pn %>%
  group_by(id, sentiment) %>%
  summarise(word_count = n(), .groups = "drop") %>%
  pivot_wider(names_from = sentiment, values_from = word_count, values_fill = 0) %>%
  right_join(comments, by = "id") %>%
  mutate(total_score = positive - negative) %>%
  mutate(sentiment = case_when(
    is.na(total_score) ~ NA_character_,
    total_score > 0 ~ "Positive",
    total_score < 0 ~ "Negative",
    TRUE ~ "Neutral"
  )) %>% arrange(id)

library(ggplot2)

ggplot(comment_sentiment_bing, aes(x = sentiment)) +
  geom_bar(fill = "blue", alpha = 0.5) +
  labs(title = "Bing Lexicon: Histogram of Sentiment", x = "Sentiment", y = "Count") +
  geom_text(stat = "count", aes(label = after_stat(count)), vjust = -0.5) +
  theme_minimal()

comparison_df2 <- comments %>% 
  left_join(comment_sentiment_bing %>% select(id, sentiment), by = "id") %>%
  rename(sentiment_bing = sentiment) %>%
  left_join(comment_sentiment %>% select(id, sentiment), by = "id") %>%
  rename(sentiment_afinn = sentiment)

comparison_df2

# show the comments where Bing and AFINN disagree

comparison_df2 %>%
  filter(sentiment_bing != sentiment_afinn | is.na(sentiment_bing) != is.na(sentiment_afinn))

# Reshape the data to long format for plotting
comparison_long <- comparison_df2 %>%
  select(id, sentiment_afinn, sentiment_bing) %>%
  pivot_longer(cols = c(sentiment_afinn, sentiment_bing),
               names_to = "lexicon",
               values_to = "sentiment") %>%
  mutate(lexicon = recode(lexicon, 
                          sentiment_afinn = "AFINN", 
                          sentiment_bing = "Bing"))

# Create a grouped bar plot to compare sentiment distributions
ggplot(comparison_long, aes(x = sentiment, fill = lexicon)) +
  geom_bar(position = "dodge", alpha = 0.5) +
  geom_text(stat = "count", 
            aes(label = after_stat(count), group = lexicon),
            position = position_dodge(width = 0.45), 
            vjust = -0.5) +
  labs(title = "Comparison of Sentiment Labels: AFINN vs Bing",
       x = "Sentiment",
       y = "Count",
       fill = "Lexicon") +
  scale_fill_manual(values = c("AFINN" = "blue", "Bing" = "red")) +
  theme_minimal()

install.packages("ollamar")

library(ollamar)

test_connection()

list_models()

#pull("llama3.2:2b")  # download a model (equivalent bash code: ollama run llama3.2:2b)

# generate a response/text based on a prompt; returns an httr2 response by default
resp <- generate(model="llama3.2:3b", prompt="tell me a 50-word story")
resp

# get just the text from the response object
resp_process(resp, "text")

# get the text as a tibble dataframe
resp_process(resp, "df")

get_sentiment_ollama <- function(text) {
  prompt <- paste("Classify the sentiment of the following text as Positive, Negative, or Neutral, and respond with only the label:", text)
  response <- generate(model = "llama3.2:3b", prompt = prompt, output="text")
  return(response)
}

library(tidyverse)

comments_ollama <- comments %>% mutate(sentiment_ollama = map_chr(text, get_sentiment_ollama))

comment_sentiment3 <- comparison_df2 %>% 
  left_join(comments_ollama %>% select(id, sentiment_ollama), by = "id")

library(tidyr)

comparison3 <- comment_sentiment3 %>%
  select(id, sentiment_afinn, sentiment_bing, sentiment_ollama) %>%
  pivot_longer(cols = c(sentiment_afinn, sentiment_bing, sentiment_ollama), 
               names_to = "method", 
               values_to = "sentiment")

comparison_counts3 <- comparison3 %>%
  count(method, sentiment)

ggplot(comparison_counts3, aes(x = sentiment, y = n, fill = method)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Sentiment Distribution Comparison", 
       x = "Sentiment", y = "Count") +
  scale_fill_manual(name = "Method",
                    values = c("#1f77b4", "#ff7f0e", "#2ca02c"), 
                    labels = c("AFINN (Lexicon)", "Bing (Lexicon)", "llama3.2:3b (LLM)")) +
  scale_y_continuous(breaks = seq(0, max(comparison_counts3$n, na.rm = TRUE) + 3, by = 3)) +
  theme_minimal()

differing_comments <- comment_sentiment3 %>%
  filter(sentiment_afinn != sentiment_ollama | sentiment_bing !=sentiment_ollama | sentiment_afinn != sentiment_ollama) %>%
  select(id, text, sentiment_afinn, sentiment_bing, sentiment_ollama)

print(differing_comments)

library(tidyverse)

real_comments <- read_csv("https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/refs/heads/master/twitter_sentiment.csv", col_names = c("id","entity", "sentiment", "text"))

head(real_comments)

library(tidytext)
real_words <- real_comments %>%
  unnest_tokens(word, text)

afinn <- get_sentiments("afinn")
bing <- get_sentiments("bing")
nrc <- get_sentiments("nrc")

# Join and score with AFINN
real_sentiment_afinn <- real_words %>%
  inner_join(afinn, by = "word") %>%
  group_by(id) %>%
  summarize(total_score = sum(value, na.rm = TRUE))

# Join and score with Bing
real_sentiment_bing <- real_words %>%
  inner_join(bing, by = "word") %>%
  group_by(id, sentiment.y) %>%
  summarize(word_count = n(), .groups = "drop") %>%
  pivot_wider(names_from = sentiment.y, values_from = word_count, values_fill = 0)

# Join and score with NRC (positive/negative)
real_sentiment_nrc <- real_words %>%
  inner_join(nrc %>% filter(sentiment %in% c("positive", "negative")), by = "word") %>%
  group_by(id, sentiment.y) %>%
  summarize(word_count = n(), .groups = "drop") %>%
  pivot_wider(names_from = sentiment.y, values_from = word_count, values_fill = 0)

# AFINN
library(ggplot2)

ggplot(real_sentiment_afinn, aes(x = id, y = total_score)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "AFINN Sentiment Scores", x = "Comment ID", y = "Score") +
  theme_minimal()

# Bing
ggplot(real_sentiment_bing, aes(x = id)) +
  geom_bar(aes(y = positive), stat = "identity", fill = "blue", alpha = 0.5) +
  geom_bar(aes(y = -negative), stat = "identity", fill = "red", alpha = 0.5) +
  labs(title = "Bing Lexicon: Positive vs Negative", x = "Comment ID", y = "Word Count") +
  theme_minimal()

# If you have Ollama and Llama3 installed:
library(ollamar)

get_sentiment_ollama <- function(text) {
  prompt <- paste("Classify the sentiment of the following text as positive, negative, or neutral, and respond with only the label in lower case:", text)
  response <- generate(model = "llama3.2:3b", prompt = prompt, output="text")
  return(response)
}

real_comments <- real_comments %>%
  mutate(sentiment_ollama = map_chr(text, get_sentiment_ollama))